Paid Ads Optimization
Diagnose wasted paid-ad spend, pacing, and allocation, then propose safe evidence-backed optimizations.
How to use it
Claude Code
- Run the line below. It pulls the whole folder into
~/.claude/skills/paid-ads-optimize. - Describe your job in plain words. Claude Code follows the skill from there.
npx degit nowork-studio/notfair-plugin/paid-ads/paid-ads-optimize#main ~/.claude/skills/paid-ads-optimizeFor one project only, change the path to .claude/skills/paid-ads-optimize.
Claude (web or desktop app)
- On this page open ⋯ → Download .md.
- Save it as SKILL.md in a folder, zip the folder, then Customize → Skills → + → Create skill → Upload a skill.
- Pick the file and Save. Claude shows the name and description and runs a security scan.
- Check the skill is switched on.
- Start a new chat and describe your job in plain words. The AI follows the skill from there.
ChatGPT or another app
- ChatGPT: make a Project and paste it into Instructions.
- Neither? Paste it at the top of a new chat — it works for that chat.
Not working?
- Check which app you pasted it into — the steps above name the right one.
- Some skills need the paid tier of Claude or ChatGPT.
Paste into Claude, ChatGPT or Cursor.
Source of Paid Ads Optimization
Show the full text26 lines
| name | description | argument-hint |
|---|---|---|
| paid-ads-optimize | Diagnose wasted paid-ad spend, pacing, and allocation, then propose safe evidence-backed optimizations. Use for waste, negatives, budgets, bid changes, poor CPA or ROAS, underpacing, overspend, or scaling decisions. | <campaign, target CPA/ROAS, or 'find waste'> |
Paid Ads Optimization
Read ../shared/operating-contract.md and ../shared/measurement-framework.md. Review before changing anything.
Diagnose before cutting
Verify the conversion signal, period completeness, spend volume, attribution model, and recent account changes. Spend with no recorded conversion can indicate broken tracking or immature data; treat it as a hypothesis until the signal and volume support an intervention. Check landing-page or operational failures before blaming targeting.
Classify the bottleneck as query/audience quality, creative fatigue, delivery/rank, budget constraint, landing-page mismatch, tracking, or economics. Use the specialized Google, Meta, X, LinkedIn, Reddit, or TikTok skill for live diagnosis. For other platforms, analyze only the supplied or verified data.
Rank reversible moves
Prefer this order: exclude an irrelevant query, placement, or audience; pause the narrowest losing unit; adjust budget or bid in a measured step; then consider structural change. For a reallocation, show the current and proposed allocations, the same total budget unless the user approves an increase, and the observable hypothesis.
Do not declare a loser from a few clicks. Set a threshold appropriate to the named target CPA, conversion lag, and channel role. Preserve upper-funnel and assisted-conversion context rather than judging all campaigns on last-click CPA alone.
Approval and follow-up
Present each exact mutation with scope, current value, proposed value, currency exposure, rationale, and review date. After approval, execute only through the verified platform skill or connector, read back the result, and record the intervention's expected effect and guardrail. Revisit after the declared observation window instead of promising a generic ongoing watch.
| 1 | |
| 2 | name paid-ads-optimize |
| 3 | description Diagnose wasted paid-ad spend, pacing, and allocation, then propose safe evidence-backed optimizations. Use for waste, negatives, budgets, bid changes, poor CPA or ROAS, underpacing, overspend, or scaling decisions. |
| 4 | argument-hint "<campaign, target CPA/ROAS, or 'find waste'>" |
| 5 | |
| 6 | |
| 7 | # Paid Ads Optimization |
| 8 | |
| 9 | Read `../shared/operating-contract.md` and `../shared/measurement-framework.md`. Review before changing anything. |
| 10 | |
| 11 | ## Diagnose before cutting |
| 12 | |
| 13 | Verify the conversion signal, period completeness, spend volume, attribution model, and recent account changes. Spend with no recorded conversion can indicate broken tracking or immature data; treat it as a hypothesis until the signal and volume support an intervention. Check landing-page or operational failures before blaming targeting. |
| 14 | |
| 15 | Classify the bottleneck as query/audience quality, creative fatigue, delivery/rank, budget constraint, landing-page mismatch, tracking, or economics. Use the specialized Google, Meta, X, LinkedIn, Reddit, or TikTok skill for live diagnosis. For other platforms, analyze only the supplied or verified data. |
| 16 | |
| 17 | ## Rank reversible moves |
| 18 | |
| 19 | Prefer this order: exclude an irrelevant query, placement, or audience; pause the narrowest losing unit; adjust budget or bid in a measured step; then consider structural change. For a reallocation, show the current and proposed allocations, the same total budget unless the user approves an increase, and the observable hypothesis. |
| 20 | |
| 21 | Do not declare a loser from a few clicks. Set a threshold appropriate to the named target CPA, conversion lag, and channel role. Preserve upper-funnel and assisted-conversion context rather than judging all campaigns on last-click CPA alone. |
| 22 | |
| 23 | ## Approval and follow-up |
| 24 | |
| 25 | Present each exact mutation with scope, current value, proposed value, currency exposure, rationale, and review date. After approval, execute only through the verified platform skill or connector, read back the result, and record the intervention's expected effect and guardrail. Revisit after the declared observation window instead of promising a generic ongoing watch. |
| 26 |
Discussion
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